Frequency selective filtering using weighted order statistic admitting real-valued weights

Abstract

In this paper, a weighted order statistic (WOS) filtering structure admitting real-valued weights is introduced. The proposed filtering approach can effectively address a number of signal and image processing applications that require robust bandpass or highpass operations where the underlying contamination follows a nonsymmetric heavy-tailed distribution. The effect of negative weighting in the filtering operation is studied under a statistical viewpoint using a weight monotonic test. Furthermore, an adaptive optimization algorithm for the design of this class of WOS filters is also introduced. Several computer simulations show the performance of the proposed filtering structure.